a

GAO

United States General Accounting Office

Report to the Ranking Minority Member, 

Subcommittee on Financial Management, 

the Budget, and International Security, 

Committee on Governmental Affairs, 

U.S. Senate

May 2004 DATA MINING

Federal Efforts Cover 

a Wide Range of Uses

GAO-04-548
Federal agencies are using data mining for a variety of purposes, ranging 

from improving service or performance to analyzing and detecting terrorist 

patterns and activities. Our survey of 128 federal departments and agencies 

on their use of data mining shows that 52 agencies are using or are planning 

to use data mining. These departments and agencies reported 199 data 

mining efforts, of which 68 are planned and 131 are operational. The figure 

here shows the most common uses of data mining efforts as described by 

agencies. Of these uses, the Department of Defense reported the largest 

number of efforts aimed at improving service or performance, managing 

human resources, and analyzing intelligence and detecting terrorist 

activities. The Department of Education reported the largest number of 

efforts aimed at detecting fraud, waste, and abuse. The National Aeronautics 

and Space Administration reported the largest number of efforts aimed at 

analyzing scientific and research information. For detecting criminal 

activities or patterns, however, efforts are spread relatively evenly among 

the agencies that reported having such efforts. 

In addition, out of all 199 data mining efforts identified, 122 used personal 

information. For these efforts, the primary purposes were improving service 

or performance; detecting fraud, waste, and abuse; analyzing scientific and 

research information; managing human resources; detecting criminal 

activities or patterns; and analyzing intelligence and detecting terrorist 

activities. 

Agencies also identified efforts to mine data from the private sector and data 

from other federal agencies, both of which could include personal 

information. Of 54 efforts to mine data from the private sector (such as 

credit reports or credit card transactions), 36 involve personal information. 

Of 77 efforts to mine data from other federal agencies, 46 involve personal 

information (including student loan application data, bank account numbers, 

credit card information, and taxpayer identification numbers). 

Top Six Purposes of Data Mining Efforts in Departments and Agencies 

Both the government and the 

private sector are increasingly 

using “data mining”—that is, the 

application of database technology 

and techniques (such as statistical 

analysis and modeling) to uncover 

hidden patterns and subtle 

relationships in data and to infer 

rules that allow for the prediction 

of future results. As has been 

widely reported, many federal data 

mining efforts involve the use of 

personal information that is mined 

from databases maintained by 

public as well as private sector 

organizations. 

GAO was asked to survey data 

mining systems and activities in 

federal agencies. Specifically, GAO 

was asked to identify planned and 

operational federal data mining 

efforts and describe their 

characteristics. 

www.gao.gov/cgi-bin/getrpt?GAO-04-548

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact Linda Koontz at 

(202) 512-6240 or koontzl@gao.gov. 

Highlights of GAO-04-548, a report to the

Ranking Minority Member, Subcommittee 

on Financial Management, the Budget, 

and International Security, Committee on 

Governmental Affairs, U.S. Senate 

May 2004

DATA MINING 

Federal Efforts Cover a Wide Range of 

Uses 
Page i GAO-04-548 Data Mining

Contents

Letter 1

Results in Brief 2

Background 3

Agencies Identified Numerous Data Mining Efforts with Various 

Aims 7

Summary 12

Appendixes

Appendix I: Objective, Scope, and Methodology 14

Appendix II: Surveyed Departments and Agencies 16

Appendix III: Departments and Agencies Reporting No Data Mining 

Efforts 23

Appendix IV: Inventories of Efforts 27

Tables Table 1: Top Six Purposes of Data Mining Efforts in Departments 

and Agencies and Number of Efforts Reported 8

Table 2: Department of Agriculture’s Inventory of Data Mining 

Efforts 27

Table 3: Department of Commerce’s Inventory of Data Mining 

Efforts 29

Table 4: Department of Defense’s Inventory of Data Mining 

Efforts 29

Table 5: Department of Education’s Inventory of Data Mining 

Efforts 37

Table 6: Department of Energy’s Inventory of Data Mining 

Efforts 40

Table 7: Department of Health and Human Services’ Inventory of 

Data Mining Efforts 41

Table 8: Department of Homeland Security’s Inventory of Data 

Mining Efforts 43

Table 9: Department of the Interior’s Inventory of Data Mining 

Efforts 46

Table 10: Department of Justice’s Inventory of Data Mining 

Efforts 47

Table 11: Department of Labor’s Inventory of Data Mining Efforts 49

Table 12: Department of State’s Inventory of Data Mining Efforts 50

Table 13: Department of Transportation’s Inventory of Data Mining 

Efforts 50
Contents

Page ii GAO-04-548 Data Mining

Table 14: Department of the Treasury’s Inventory of Data Mining 

Efforts 51

Table 15: Department of Veterans Affairs’ Inventory of Data Mining 

Efforts 54

Table 16: Environmental Protection Agency’s Inventory of Data 

Mining Efforts 56

Table 17: Export-Import Bank of the United States’ Inventory of Data 

Mining Efforts 56

Table 18: Federal Deposit Insurance Corporation’s Inventory of Data 

Mining Efforts 57

Table 19: Federal Reserve System’s Inventory of Data Mining 

Efforts 57

Table 20: National Aeronautics and Space Administration’s 

Inventory of Data Mining Efforts 58

Table 21: Nuclear Regulatory Commission’s Inventory of Data 

Mining Efforts 62

Table 22: Office of Personnel Management’s Inventory of Data 

Mining Efforts 62

Table 23: Pension Benefit Guaranty Corporation’s Inventory of Data 

Mining Efforts 63

Table 24: Railroad Retirement Board’s Inventory of Data Mining 

Efforts 63

Table 25: Small Business Administration’s Inventory of Data Mining 

Efforts 64

Figures Figure 1: Top Six Purposes of Data Mining Efforts That Involve 

Personal Information 10

Figure 2: Top Six Purposes of Data Mining Efforts That Involve 

Private Sector Data 11

Figure 3: Top Six Purposes of Data Mining Efforts That Involve 

Data from Other Federal Agencies 12
Contents

Page iii GAO-04-548 Data Mining

Abbreviations

CARDS Counterintelligence Analytical Research Data System

CG Coast Guard 

CI-AIMS Counterintelligence Automated Investigative 

Management System 

DHHS Department of Health and Human Services

DOD Department of Defense

DOE Department of Energy

DOT Department of Transportation

EFTPS Electronic Federal Tax Payment System 

EOS Earth Observing System 

FARS Fatality Analysis Reporting System 

FDA Food and Drug Administration

GENESIS Global Environmental and Earth Science Information

System 

GSFC Goddard Space Federal Center 

HR Human Resources

HRSA Health Resources and Services Administration 

MATRIX Multistate Anti-terrorism Information Exchange System

NASA National Aeronautics and Space Administration

NVO National Virtual Observatory 

OIG Office of Inspector General

OLAP On-line Analytical Processing 

RSST Real Estate Stress Test 

SAA Spectral Analysis Automation 

SAS Safety Automated System 

SMARTS Statistical Management Analysis and Reporting Tool

System

SWC Space Warfare Center

TIMS Technical Information Management System 

TOP Treasury Offset Program 

VA Veterans Affairs

VHA Veterans Health Administration 

VISN Veterans Integrated Service Network

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Page 1 GAO-04-548 Data Mining

United States General Accounting Office

Washington, D.C. 20548

A

May 4, 2004 Letr

The Honorable Daniel K. Akaka 

Ranking Minority Member 

Subcommittee on Financial Management, the Budget,

and International Security 

Committee on Governmental Affairs 

United States Senate

Dear Senator Akaka:

Data mining—a technique for extracting knowledge from large volumes of 

data—is increasingly being used by government and by the private sector. 

As has been widely reported, many federal data mining efforts involve the 

use of personal information1

 that is mined from public as well as private 

sector organizations. 

This report responds to your request that we identify and describe 

operational and planned data mining systems and activities in federal 

agencies. In a follow-up report, we plan to perform an in-depth review of 

selected federal data mining efforts. 

The term “data mining” has a number of meanings. For purposes of this 

work, we define data mining as the application of database technology and 

techniques—such as statistical analysis and modeling—to uncover hidden 

patterns and subtle relationships in data and to infer rules that allow for the 

prediction of future results. We based this definition on the most commonly 

used terms found in a survey of the technical literature. In our initial survey 

of chief information officers, these officials found the definition sufficient 

to identify agency data mining efforts. 

1

As used in this report, personal information is all information associated with an individual 

and includes both identifying information and nonidentifying information. Identifying 

information, which can be used to locate or identify an individual, includes name, aliases, 

Social Security number, e-mail address, driver’s license number, and agency-assigned case 

number. Nonidentifying personal information includes age, education, finances, criminal 

history, physical attributes, and gender.
Page 2 GAO-04-548 Data Mining

To address our objective to identify and describe operational and planned 

data mining systems and activities in federal agencies, we surveyed chief 

information officers or comparable officials at 128 federal departments and 

agencies to determine whether the agencies had operational and planned 

data mining systems or activities.2

 We then conducted telephone interviews 

with the reported system managers to obtain information on the 

characteristics of the identified data mining efforts. To verify the 

information we received, we sent follow-up letters to agencies that 

responded as well as to those that did not respond, we asked responsible 

officials to verify the information, and we performed random assessments 

of the means that these officials used to verify the information.

In addition, we conducted a search of technical literature and periodicals 

to develop a comprehensive list of federal government data mining efforts 

and then compared these efforts with data mining efforts reported by 

federal agencies. If the data mining efforts on our lists were not reported on 

the survey, we contacted the appropriate chief information officers and, 

with their concurrence, added the efforts.

We performed our work from May 2003 to April 2004 in accordance with 

generally accepted government auditing standards. Additional details on 

our scope and methodology are provided in appendix I.

Results in Brief Federal agencies are using data mining for a variety of purposes, ranging 

from improving service or performance to analyzing and detecting terrorist 

patterns and activities. Our survey of 128 federal departments and agencies 

on their use of data mining shows that 52 agencies are using or are planning 

to use data mining. These departments and agencies reported 199 data 

mining efforts, of which 68 were planned and 131 were operational. The 

most common uses of data mining efforts were described by agencies as

• improving service or performance; 

• detecting fraud, waste, and abuse; 

• analyzing scientific and research information;

2

That is, we asked about both systems explicitly dedicated to data mining and activities 

using automated tools to “mine” databases that are part of other systems. In this report, we 

use the word “efforts” to refer to both systems and activities, unless otherwise specified.
Page 3 GAO-04-548 Data Mining

• managing human resources;

• detecting criminal activities or patterns; and

• analyzing intelligence and detecting terrorist activities.

The Department of Defense reported having the largest number of data 

mining efforts aimed at improving service or performance and at managing 

human resources. Defense was also the most frequent user of efforts aimed 

at analyzing intelligence and detecting terrorist activities, followed by the 

Departments of Homeland Security, Justice, and Education. 

The Department of Education reported the largest number of efforts aimed 

at detecting fraud, waste, and abuse, while the National Aeronautics and 

Space Administration targets most of their data mining efforts (21 out of 

23) toward analyzing scientific and research information. Data mining 

efforts for detecting criminal activities or patterns, however, were spread 

relatively evenly among the reporting agencies. 

In addition, out of all 199 data mining efforts identified, 122 used personal 

information. For these efforts, the primary purposes were detecting fraud, 

waste, and abuse; detecting criminal activities or patterns; analyzing 

intelligence and detecting terrorist activities; and increasing tax 

compliance. 

Agencies also identified efforts to mine data from the private sector and 

data from other federal agencies, both of which could include personal 

information. Of 54 efforts to mine data from the private sector (such as 

credit reports or credit card transactions), 36 involve personal information. 

Of 77 efforts to mine data from other federal agencies, 46 involve personal 

information (including student loan application data, bank account 

numbers, credit card information, and taxpayer identification numbers).

Background Data mining enables corporations and government agencies to analyze 

massive volumes of data quickly and relatively inexpensively. The use of 

this type of information retrieval has been driven by the exponential 

growth in the volumes and availability of information collected by the 

public and private sectors, as well as by advances in computing and data 

storage capabilities. In response to these trends, generic data mining tools 

are increasingly available for—or built into—major commercial database 

applications. Today, mining can be performed on many types of data, 
Page 4 GAO-04-548 Data Mining

including those in structured, textual, spatial, Web, or multimedia forms. 

Data mining is becoming a big business; Forrester Research has estimated 

that the data mining market is passing the billion dollar mark.

Although the use and sophistication of data mining have increased in both 

the government and the private sector, data mining remains an ambiguous 

term. According to some experts, data mining overlaps a wide range of 

analytical activities, including data profiling, data warehousing, online 

analytical processing, and enterprise analytical applications.3

 Some of the 

terms used to describe data mining or similar analytical activities include 

“factual data analysis” and “predictive analytics.” We surveyed technical 

literature and developed a definition of data mining based on the most 

commonly used terms found in this literature. Based on this search, we 

define data mining as the application of database technology and 

techniques—such as statistical analysis and modeling—to uncover hidden 

patterns and subtle relationships in data and to infer rules that allow for the 

prediction of future results. We used this definition in our initial survey of 

chief information officers; these officials found the definition sufficient to 

identify agency data mining efforts.

Data mining has been used successfully for a number of years in the private 

and public sectors in a broad range of applications. In the private sector, 

these applications include customer relationship management, market 

research, retail and supply chain analysis, medical analysis and diagnostics, 

financial analysis, and fraud detection. In the government, data mining was 

initially used to detect financial fraud and abuse. For example, data mining 

has been an integral part of GAO audits and investigations of federal 

government purchase and credit card programs.4

 Data mining and related 

technologies are also emerging as key tools in Department of Homeland 

Security initiatives.

3

Lou Agosta, “Data Mining Is Dead—Long Live Predictive Analytics!” (Forrester Research, 

Oct. 30, 2003), http://www.forrester.com/Research/LegacyIT/0,7208,33030,00.html

(downloaded Jan. 26, 2004).

4

For more information on the uses of data mining in GAO audits, see U.S. General 

Accounting Office, Data Mining: Results and Challenges for Government Programs, 

Audits, and Investigations, GAO-03-591T (Washington, D.C: Mar. 25, 2003).
Page 5 GAO-04-548 Data Mining

Data Mining Poses Privacy 

Challenge

Since the terrorist attacks of September 11, 2001, data mining has been 

seen increasingly as a useful tool to help detect terrorist threats by 

improving the collection and analysis of public and private sector data. In a 

recent report on information sharing and analysis to address the challenges 

of homeland security, it was noted that agencies at all levels of government 

are now interested in collecting and mining large amounts of data from 

commercial sources.5

 The report noted that agencies may use such data not 

only for investigations of known terrorists, but also to perform large-scale 

data analysis and pattern discovery in order to discern potential terrorist 

activity by unknown individuals. Such use of data mining by federal 

agencies has raised public and congressional concerns regarding privacy.

One example of a large-scale development effort launched in the wake of 

the September 11 attacks is the Multistate Anti-terrorism Information 

Exchange System, known as MATRIX. MATRIX, currently used in five 

states,6

 provides the capability to store, analyze, and exchange sensitive 

terrorism-related and other criminal intelligence data among agencies 

within a state, among states, and between state and federal agencies. 

Information in MATRIX databases includes criminal history records, 

driver’s license data, vehicle registration records, incarceration records, 

and digitized photographs. Public awareness of MATRIX and of similar 

large-scale data mining or data mining-like projects has led to concerns 

about the government’s use of data mining to conduct a mass 

“dataveillance”7

—a surveillance of large groups of people—to sift through 

vast amounts of personally identifying data to find individuals who might fit 

a terrorist profile.

5

Creating a Trusted Information Network for Homeland Security (New York City: The 

Markle Foundation, December 2003), 

http://www.markletaskforce.org/Report2_Full_Report.pdf (downloaded Mar. 8, 2004).

6

Five states are currently participating in the MATRIX pilot project: Connecticut, Florida, 

Michigan, Ohio, and Pennsylvania.

7

Roger Clarke, “Information Technology and Dataveillance,” Communications of the ACM, 

vol. 31, issue 5 (New York City: ACM Press, May 1988), 

http://www.anu.edu.au/people/Roger.Clarke/DV/CACM88.html (downloaded Mar. 5, 2004). 

Clarke defines mass dataveillance as the systematic use of personal data systems in the 

investigation or monitoring of the actions or communications of groups of people.
Page 6 GAO-04-548 Data Mining

Mining government and private databases containing personal information 

creates a range of privacy concerns. Through data mining, agencies can 

quickly and efficiently obtain information on individuals or groups by 

exploiting large databases containing personal information aggregated 

from public and private records. Information can be developed about a 

specific individual or about unknown individuals whose behavior or 

characteristics fit a specific pattern. Before data aggregation and data 

mining came into use, personal information contained in paper records 

stored at widely dispersed locations, such as courthouses or other 

government offices, was relatively difficult to gather and analyze. As one 

expert noted, data mining technologies that provide for easy access and 

analysis of aggregated data challenge the concept of privacy protection 

afforded to individuals through the inherent inefficiency of government 

agencies analyzing paper, rather than aggregated, computer records.8

Privacy concerns about mined or analyzed personal data also include 

concerns about the quality and accuracy of the mined data; the use of the 

data for other than the original purpose for which the data were collected 

without the consent of the individual; the protection of the data against 

unauthorized access, modification, or disclosure; and the right of 

individuals to know about the collection of personal information, how to 

access that information, and how to request a correction of inaccurate 

information.

9

8

K.A. Taipale, “Data Mining and Domestic Security: Connecting the Dots to Make Sense of 

Data,” The Columbia Science and Technology Law Review, vol. V, 2003-2004 (New York 

City: Columbia Law School, 2004), http://www.stlr.org/cite.cgi?volume=5&article=2

(downloaded Mar. 18, 2004).

9

These privacy concerns are reflected in the Fair Information Practices proposed in 1980 by 

the Organization for Economic Cooperation and Development and endorsed by the U.S. 

Department of Commerce in 1981. These practices govern collection limitation, purpose 

specification, use limitation, data quality, security safeguards, openness, individual 

participation, and accountability.
Page 7 GAO-04-548 Data Mining

Agencies Identified 

Numerous Data Mining 

Efforts with Various 

Aims

Of 128 federal departments and agencies surveyed for information on their 

planned and operational data mining efforts (listed in app. II), 52 agencies 

reported 199 data mining efforts, and 69 agencies reported that they were 

not engaged in data mining and were not planning such efforts (listed in 

app. III). Of the 199 data mining efforts, 68 were planned and 131 were 

operational. Seven agencies did not respond to our survey.10 Appendix IV 

lists the 199 data mining efforts reported, along with key characteristics.

Agencies described the most common purposes of data mining efforts as

• improving service or performance; 

• detecting fraud, waste, and abuse; 

• analyzing scientific and research information;

• managing human resources; 

• detecting criminal activities or patterns; and

• analyzing intelligence and detecting terrorist activities.

As shown in table 1, the Department of Defense reported the largest 

number of efforts aimed at improving service or performance (with 19 out 

of 65 reported efforts) and at managing human resources (with 14 out of 17 

efforts). Defense was also the most frequent user of efforts aimed at 

analyzing intelligence and detecting terrorist activities, with 5 of 14 efforts, 

followed by the Departments of Homeland Security and Justice, with 4 and 

3 efforts, respectively. The Department of Education has the largest 

number of efforts aimed at detecting fraud, waste, and abuse (9 out of 24 

efforts reported). The National Aeronautics and Space Administration 

accounts for 21 of the 23 identified efforts for analyzing scientific and 

research information. Efforts are spread relatively evenly among the 

agencies that reported using data mining efforts for detecting criminal 

10Agencies that did not respond to our survey are (1) the Central Intelligence Agency; (2) the 

Corporation for National and Community Services; (3) the Department of Army, Department 

of Defense; (4) the Equal Employment Opportunity Commission; (5) the National Park 

Service, Department of the Interior; (6) the National Security Agency, Department of 

Defense; and (7) the Rural Utilities Service, Department of Agriculture.
Page 8 GAO-04-548 Data Mining

activities or patterns. Table 1 summarizes the top six uses of data mining 

efforts among the responding agencies.

Table 1: Top Six Purposes of Data Mining Efforts in Departments and Agencies and Number of Efforts Reported 

Source: GAO analysis of agency-provided data.

Department or agency

Improving 

service or 

performance

Detecting 

fraud, waste, 

and abuse

Analyzing 

scientific and 

research 

information

Managing 

human 

resources

Detecting 

criminal 

activities or 

patterns

Analyzing 

intelligence 

and detecting 

terrorist 

activities

Department of Agriculture 8 1

Department of Commerce

Department of Defense 19 1 1 14 1 5

Department of Education 6 9 3 1

Department of Energy 3

Department of Health and Human 

Services 4 1 1

Department of Homeland Security 5 2 2 4

Department of the Interior 1

Department of Justice 1 1 3 3

Department of Labor 3 1

Department of State 2

Department of Transportation 1

Department of the Treasury 4 1 2

Department of Veterans Affairs 5 5 1

Environmental Protection Agency 1

Export-Import Bank of the United 

States 1

Federal Deposit Insurance Corporation 1

Federal Reserve System 1

National Aeronautics and Space 

Administration 1 1 21

Nuclear Regulatory Commission 1

Office of Personnel Management 1

Pension Benefit Guaranty Corporation 2

Railroad Retirement Board 1

Small Business Administration 1

Total 65 24 23 17 15 14
Page 9 GAO-04-548 Data Mining

Some data mining purposes focus on human activities and therefore are 

inherently likely to involve personal information; examples of these 

purposes are detecting fraud, waste, and abuse; detecting criminal 

activities or patterns; managing human resources; and analyzing 

intelligence. The following are examples of data mining efforts for each of 

these purposes: 

• Detecting fraud, waste, and abuse. The Veterans Benefits 

Administration’s C & P Payment Data Analysis effort mines veterans’ 

compensation and pension data for evidence of fraud.

• Detecting criminal activities or patterns. The Department of 

Education’s Title IV Identity Theft Initiative effort focuses on identity 

theft cases involving education loans. 

• Managing human resources. The U.S. Air Force’s Oracle HR (Human 

Resources) uses data mining to provide information on promotions, pay 

grades, clearances, and other information relevant to human resources 

planning. 

• Analyzing intelligence and detecting terrorist activities. The Defense 

Intelligence Agency’s Verity K2 Enterprise mines data from the 

intelligence community and Internet sources to identify foreign 

terrorists or U.S. citizens connected to foreign terrorism activities.

On the other hand, other categories of efforts do not necessarily focus on 

human activities or involve personal information, such as many of the 

efforts aimed at analyzing scientific and research information. The National 

Aeronautics and Space Administration, for example, mines large, complex 

earth science data sets to find patterns and relationships to detect hidden 

events (the system is called Machine Learning and Data Mining for 

Improved Data Understanding of High Dimensional Earth Sensed Data). 

Similarly, many efforts aimed at improving service or performance (the 

most frequently cited purpose of data mining efforts) do not involve 

personal information. For example, the Department of the Navy’s Supply 

Management System Multidimensional Cubes system includes a data 

warehouse containing data on every ship part that has been ordered since 

the 1980s, with multidimensional information on each part. The Navy uses 

data mining to calculate failure rates and identify needed improvements; 

according to the Navy, this system reduces downtime on ships by 

improving parts replacement. 
Page 10 GAO-04-548 Data Mining

However, some efforts aimed at improving service or performance do 

involve personal information. For example, the Veterans Administration’s 

VISN (Veterans Integrated Service Network) 16 Data Warehouse is mined 

for a variety of information, including patient visits, laboratory tests, and 

pharmacy records, to provide management with health care system 

performance information. 

Overall, 122 of the 199 data mining efforts involve personal information. 

Figure 1 shows the top six purposes of these efforts, as well as their 

distribution.

Figure 1: Top Six Purposes of Data Mining Efforts That Involve Personal Information

Of the 199 data mining efforts, 54 use or plan to use data from the private 

sector. Of these, 36 involve personal information. The personal information 

from the private sector included credit reports and credit card transaction 

records. Figure 2 shows the distribution of the top six purposes of the 54 

efforts involving data from the private sector. 

0 10 20 30 40

Purposes

Source: GAO analysis of agency data.

Number of data mining efforts

Managing human resources

Analyzing intelligence and detecting 

terrorist activities

Increasing tax compliance

Detecting criminal activities or 

patterns

Improving service or performance

Detecting fraud, waste, and abuse

7

10

15

15

24

33
Page 11 GAO-04-548 Data Mining

Figure 2: Top Six Purposes of Data Mining Efforts That Involve Private Sector Data

Of the 199 data mining efforts, 77 efforts use or plan to use data from other 

federal agencies. Of the 77 efforts, 46 involve personal information. The 

personal information from other federal agencies included student loan 

application data, bank account numbers, credit card information, and 

taxpayer identification numbers. Figure 3 shows the top six uses for the 77 

efforts involving data from other federal agencies and their distribution. 

0 10 20 30 40

Purposes

Source: GAO analysis of agency data.

Number of data mining efforts

Improving safety

Detecting criminal activities or 

patterns

Analyzing intelligence and 

detecting terrorist activities

Analyzing scientific and research 

information

Detecting fraud, waste, and abuse

Improving service or performance

4

4

5

8

9

14
Page 12 GAO-04-548 Data Mining

Figure 3: Top Six Purposes of Data Mining Efforts That Involve Data from Other 

Federal Agencies

Summary Driven by advances in computing and data storage capabilities and by 

growth in the volumes and availability of information collected by the 

public and private sectors, data mining enables government agencies to 

analyze massive volumes of data. Our survey shows that data mining is 

increasingly being used by government for a variety of purposes, ranging 

from improving service or performance to analyzing and detecting terrorist 

patterns and activities. 

Although this survey provides a broad overview of the emerging uses of 

data mining in the federal government, more work is needed to shed light 

on the privacy implications of these efforts. In future work, we plan to 

examine selected federal data mining efforts and their implications.

As agreed with your office, unless you publicly announce the contents of 

the report earlier, we plan no further distribution until 30 days from the 

report date. At that time, we will send copies of this report to the Chairmen 

and Ranking Minority Members of the House Committee on Government 

Reform; Subcommittee on Civil Service and Agency Organization, House 

Committee on Government Reform; Select Committee on Homeland 

Security, House of Representatives; Senate Committee on Governmental 

0 10 20 30 40

Purposes

Source: GAO analysis of agency data.

Number of data mining efforts

Detecting fraud, waste, and abuse

Analyzing scientific and research 

information

Analyzing intelligence and detecting 

terrorist activities

Detecting criminal activities or patterns

Managing human resources

Improving service or performance

5

6

7

12

13

20
Page 13 GAO-04-548 Data Mining

Affairs; and the Subcommittee on Oversight of Government Management, 

the Federal Workforce and the District of Columbia, Senate Committee on 

Governmental Affairs. We will also make copies available to others on 

request. In addition, this report will be available at no charge on the GAO 

Web site at http://www.gao.gov.

If you have any questions concerning this report, please call me at (202) 

512-6240 or Mirko J. Dolak, Assistant Director, at (202) 512-6362. We can 

also be reached by e-mail at koontzl@gao.gov and dolakm@gao.gov, 

respectively. Key contributors to this report were Camille M. Chaires, 

Barbara S. Collier, Orlando O. Copeland, Nancy E. Glover, Stuart M. 

Kaufman, Lori D. Martinez, Morgan F. Walts, and Marcia C. Washington.

Sincerely yours,

Linda D. Koontz

Director, Information Management Issues
Page 14 GAO-04-548 Data Mining

Appendix I

Objective, Scope, and Methodology ApApenednxidexiIs

Our objective was to identify and describe planned and operational federal 

data mining efforts. As a first step in addressing this objective, we 

developed a definition of “data mining.” Because this expression has a 

range of meanings, we surveyed the technical literature to develop a 

definition based on the most commonly used terms found in this literature. 

We defined data mining as the application of database technology and 

techniques—such as statistical analysis and modeling—to uncover hidden 

patterns and subtle relationships in data and to infer rules that allow for the 

prediction of future results. In our initial survey of chief information 

officers, these officials found the definition sufficient to identify agency 

data mining efforts. 

We then surveyed chief information officers or comparable officials at 128 

federal departments and agencies (see app. II) and asked them to identify 

whether their agency had operational and planned data mining efforts. We 

achieved a 95 percent response rate. Of the 121 agencies that responded, 69 

reported that they did not have any data mining efforts (see app. III). We 

followed up with these 69 agencies and gave them another opportunity to 

report data mining efforts. 

To obtain information on the characteristics of the identified operational or 

planned data mining efforts, we conducted structured telephone 

interviews1

 with the identified system owners or activity managers. The 

interviews were designed to obtain detailed information about each data 

mining system, including the purpose and size, the use of personal 

information, and the use of data from the private sector or other federal 

organizations. We pretested the structured interview to ensure relevance 

and clarity.

We aggregated these data by agency and sent them back to the chief 

information officer, comparable official, or their designee and asked that 

they review the characteristics for completeness and accuracy. One of the 

52 departments and agencies that reported data mining systems—the 

Department of Homeland Security—has not responded to our request to 

review the reported data for completeness and accuracy.

1

In a structured interview, the interviewer asks the same questions of numerous individuals 

or individuals representing numerous organizations in a precise manner, offering each 

interviewee the same set of possible responses.
Appendix I

Objective, Scope, and Methodology

Page 15 GAO-04-548 Data Mining

We performed random assessments of the means that these officials used 

to verify the information. Based on these assessments, we concluded that 

the agencies’ verification methods were reasonable and that as a result, we 

could rely on the accuracy of the reported data. We also conducted a 

search of technical literature and periodicals to develop a list of federal 

government data mining efforts and then compared the efforts on this list 

with the data mining efforts reported by federal agencies. If the data mining 

efforts on our list were not reported on the survey, we contacted the chief 

information officer or comparable official to determine whether that data 

mining effort should be included in our survey. 

Because this was not a sample survey, there are no sampling errors. 

However, the practical difficulties of conducting any survey may introduce 

errors, commonly referred to as nonsampling errors. For example, 

difficulties in how a particular question is interpreted, in the sources of 

information that are available to respondents, or in how the data are 

entered into a database or were analyzed can introduce unwanted 

variability into the survey results. We took steps in the development of the 

structured interview, the data collection, and the data analysis to minimize 

these nonsampling errors. Among these steps, we pretested the structured 

interview instrument, contacted nonresponding agencies as well as 

agencies not identifying data mining efforts, and sent the aggregated data 

to the agency chief information officer for review.

We conducted our work from May 2003 to April 2004 in accordance with 

generally accepted government auditing standards.
Page 16 GAO-04-548 Data Mining

Appendix II

Surveyed Departments and Agencies ApendxiI

Department of Agriculture

• Agricultural Marketing Service

• Agricultural Research Service

• Animal and Plant Health Inspection Service

• Cooperative State Research, Education, and Extension Service

• Farm Service Agency

• Food and Nutrition Service

• Food Safety and Inspection Service

• Foreign Agricultural Service

• Forest Service

• National Agricultural Statistics Service

• Natural Resources Conservation Service

• Risk Management Agency

• Rural Utilities Service

Department of Commerce

• Bureau of the Census

• Economic Development Administration

• International Trade Administration

• National Oceanic and Atmospheric Administration

• U.S. Patent and Trademark Office
Appendix II

Surveyed Departments and Agencies

Page 17 GAO-04-548 Data Mining

Department of Defense

• Missile Defense Agency

• Defense Advanced Research Projects Agency

• Defense Commissary Agency

• Defense Contract Audit Agency

• Defense Contract Management Agency

• Defense Information Systems Agency

• Defense Intelligence Agency

• Defense Legal Services Agency

• Defense Logistics Agency

• Defense Security Cooperation Agency

• Defense Security Service

• Defense Threat Reduction Agency

• Department of the Air Force

• Department of the Army

• Department of the Navy

• National Geospatial-Intelligence Agency

• National Security Agency

• U.S. Marine Corps

Department of Education
Appendix II

Surveyed Departments and Agencies

Page 18 GAO-04-548 Data Mining

Department of Energy

• Bonneville Power Administration 

• Southeastern Power Administration

• Southwestern Power Administration

• Western Area Power Administration

Department of Health and Human Services

• Administration for Children and Families

• Agency for Healthcare Research and Quality

• Centers for Disease Control and Prevention

• Centers for Medicare and Medicaid Services

• Food and Drug Administration

• Health Resources and Services Administration

• Indian Health Service

• National Institutes of Health

• Program Support Center

Department of Homeland Security

• Border and Transportation Security Directorate

• Bureau of Citizenship and Immigration Services

• Emergency Preparedness and Response Directorate

• Information Analysis and Infrastructure Protection Directorate 

• Management Directorate
Appendix II

Surveyed Departments and Agencies

Page 19 GAO-04-548 Data Mining

• Science and Technology Directorate

• U.S. Coast Guard

• U.S. Secret Service

Department of Housing and Urban Development

Department of the Interior

• Bureau of Indian Affairs

• Bureau of Land Management

• Bureau of Reclamation

• Minerals Management Service

• National Park Service

• Office of Surface Mining Reclamation and Enforcement

• U.S. Fish and Wildlife Service

• U.S. Geological Survey

Department of Justice

• Bureau of Alcohol, Tobacco, Firearms, and Explosives

• Drug Enforcement Administration

• Federal Bureau of Investigation

• Federal Bureau of Prisons

• U.S. Marshals Service

Department of Labor

Department of State
Appendix II

Surveyed Departments and Agencies

Page 20 GAO-04-548 Data Mining

Department of Transportation

• Federal Aviation Administration

• Federal Highway Administration

• Federal Motor Carrier Safety Administration 

• Federal Railroad Administration

• Federal Transit Administration

• National Highway Traffic Safety Administration

Department of the Treasury

• Bureau of Engraving and Printing

• Bureau of the Public Debt

• Financial Management Service

• Internal Revenue Service

• Office of the Comptroller of the Currency

• Office of Thrift Supervision

• U.S. Mint

Department of Veterans Affairs

• Veterans Benefits Administration

• Veterans Health Administration

Agency for International Development

Central Intelligence Agency

Corporation for National and Community Service
Appendix II

Surveyed Departments and Agencies

Page 21 GAO-04-548 Data Mining

Environmental Protection Agency

Equal Employment Opportunity Commission

Executive Office of the President

Export-Import Bank of the United States

Federal Deposit Insurance Corporation

Federal Energy Regulatory Commission

Federal Reserve System

Federal Retirement Thrift Investment Board

General Services Administration

Legal Services Corporation

National Aeronautics and Space Administration

National Credit Union Administration

National Labor Relations Board

National Science Foundation

Nuclear Regulatory Commission

Office of Management and Budget

Office of Personnel Management

Peace Corps

Pension Benefit Guaranty Corporation

Railroad Retirement Board

Securities and Exchange Commission
Appendix II

Surveyed Departments and Agencies

Page 22 GAO-04-548 Data Mining

Small Business Administration

Smithsonian Institution

Social Security Administration

U.S. Postal Service
Page 23 GAO-04-548 Data Mining

Appendix III

Departments and Agencies Reporting No Data 

Mining Efforts ApendxiI

The following 69 departments and agencies reported that they have no 

operational or planned data mining efforts:

Department of Agriculture

• Agricultural Marketing Service

• Agricultural Research Service

• Animal and Plant Health Inspection Service

• Cooperative State Research, Education, and Extension Service

• Farm Service Agency

• Foreign Agricultural Service

• Forest Service

• National Agricultural Statistics Service

• Food Safety and Inspection Service

Department of Commerce

• Economic Development Administration

• Bureau of the Census

• International Trade Administration

• Department of Commerce Headquarters

• National Oceanic and Atmospheric Administration

Department of Defense

• Defense Contract Audit Agency

• Missile Defense Agency

• Defense Legal Services Agency
Appendix III

Departments and Agencies Reporting No 

Data Mining Efforts

Page 24 GAO-04-548 Data Mining

• Defense Security Service

• Defense Threat Reduction Agency

• Defense Logistics Agency

• Defense Advanced Research Projects Agency

• Defense Contract Management Agency

• Defense Security Cooperation Agency

Department of Energy

• Bonneville Power Administration

• Southeastern Power Administration

• Southwestern Power Administration 

• Western Area Power Administration

Department of Health and Human Services

• Centers for Medicare and Medicaid Services

• Administration for Children and Families

• National Institutes of Health

• Indian Health Service

Department of Homeland Security

• Science and Technology Directorate

• Management Directorate

• Bureau of Citizenship and Immigration Services

• Department of Homeland Security Headquarters
Appendix III

Departments and Agencies Reporting No 

Data Mining Efforts

Page 25 GAO-04-548 Data Mining

Department of Housing and Urban Development

Department of the Interior

• Bureau of Reclamation

• Bureau of Land Management

• U.S. Geological Survey

• Fish and Wildlife Service

• Office of Surface Mining Reclamation and Enforcement

• Bureau of Indian Affairs

• Department of the Interior Headquarters

Department of Justice

• Bureau of Alcohol, Tobacco, Firearms, and Explosives

Department of Transportation

• Federal Aviation Administration

• Federal Transit Administration

• Federal Railroad Administration

• Federal Motor Carrier Safety Administration

• Federal Highway Administration

Department of the Treasury

• Comptroller of the Currency

• Bureau of the Public Debt

• Office of Thrift Supervision
Appendix III

Departments and Agencies Reporting No 

Data Mining Efforts

Page 26 GAO-04-548 Data Mining

• Department of the Treasury Headquarters

• Bureau of Engraving and Printing

Agency for International Development

Executive Office of the President

Federal Energy Regulatory Commission

Federal Retirement Thrift Investment Board

General Services Administration

Legal Services Corporation

National Credit Union Administration

National Labor Relations Board

National Science Foundation

Office of Management and Budget

Peace Corps

Security and Exchange Commission

Smithsonian Institution

Social Security Administration

U.S. Postal service
Page 27 GAO-04-548 Data Mining

Appendix IV

Inventories of Efforts ApendxiIV

The following tables present selected information from our survey of 128 

major federal departments and agencies on their use of data mining. The 

tables list the purpose of each data mining effort, whether the system is 

planned or operational, and whether the system uses personal information, 

data from the private sector, or data from other federal agencies. The 

survey shows that 52 departments and agencies are using or are planning to 

use data mining. These departments and agencies reported 199 data mining 

efforts, of which 68 were planned and 131 were operational. 

Table 2: Department of Agriculture’s Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency 

data

Department of Agriculture Headquarters

Travel Data Mart Will consolidate employee travel 

information from financial and 

travel systems. Will allow for a 

governmentwide e-travel system 

and provide the department with 

information on the financial 

ramifications of its travel. 

Improving 

service or 

performance 

Planned Yes No No 

Financial Statements 

Data Warehouse 

Is used in the production of 

consolidated financial statements. 

Provides information for products 

that are used to satisfy external 

reporting requirements, such as 

Office of Management and Budget 

and Department of the Treasury 

requirements. 

Financial 

management 

Operational No No No 

Financial Data 

Warehouse 

Is the department’s internal 

financial management reporting 

system. Data mining is done for ad 

hoc and on-demand reports. 

Financial 

management 

Operational Yes No No 

Food and Nutrition Service 

Grantee Monitoring 

Activities—Southeast 

Regional Office 

Assists in monitoring the financial 

status of grant holders. Grantees 

are required to provide 

expenditure reports, and analysis 

is performed quarterly that 

matches stated draws to the 

actual draws from the U.S. 

Treasury. 

Improving 

service or 

performance 

Operational Yes No No 
Appendix IV

Inventories of Efforts

Page 28 GAO-04-548 Data Mining

Source: Department of Agriculture.

Grantee Monitoring 

Activities—Mountain 

Plains Regional 

Office 

Assists in monitoring the 

management and distribution of 

Indian funds for major food benefit 

programs, such as food stamps, in 

10 grantee states. 

Improving 

service or 

performance 

Operational Yes No No 

Grantee Monitoring 

Activities—

Southwest Regional 

Office 

Maximizes on-site monitoring 

efforts by confirming the accuracy 

of grantee accounting. Reduces 

on-site time, maximizes time to 

complete reviews, and has 

achieved a 50 percent travel 

savings. 

Improving 

service or 

performance 

Operational Yes No No 

Grantee Monitoring 

Activities—Midwest 

Regional Office 

Will be a reporting system to 

provide reports and automate the 

audit process. Plans are to 

acquire data mining tools to review 

and compare budgets, reports, 

and plans. 

Improving 

service or 

performance 

Planned No No Yes 

Grantee Monitoring 

Activities—Northeast 

Regional Office 

Supports on-site reviews of 

analyses to confirm financial 

report information. 

Improving 

service or 

performance 

Operational Yes Yes No 

Integrated Program 

Accounting System 

Data Integrity 

Will create ad-hoc reporting 

centers to validate accounting 

information. 

Improving 

service or 

performance 

Planned No No No 

Natural Resources Conservation Service 

National Resource 

Inventory Used for 

Statistical Analysis of 

Past Soil Survey 

Databases. 

Is a trending database that tracks 

more than 200 resource issues 

such as monitoring erosion. Also 

processes statistical technology. 

Improving 

service or 

performance 

Operational No No No 

Risk Management Agency

CAE Is part of a congressionally 

mandated project to assist the 

Risk Management Agency in 

controlling fraud, waste, and 

abuse in the Federal Crop 

Insurance Corporation program. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes Yes 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency 

data
Appendix IV

Inventories of Efforts

Page 29 GAO-04-548 Data Mining

Table 3: Department of Commerce’s Inventory of Data Mining Efforts

Source: Department of Commerce.

Table 4: Department of Defense’s Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency 

data

U.S. Patent and Trademark Office

Compensation 

Projection Model in 

the Enterprise Data 

Warehouse 

Generates and makes available 

compensation projection data, 

both salary and benefits, on 

current employees and on 

planned hires. It also accounts for 

planned attritions. 

Managing 

human 

resources 

Operational Yes No Yes 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Defense Commissary Agency

DeCA Electronic 

Records 

Management and 

Archive System 

Will be a corporate information 

system for managing unstructured 

data. It will allow for electronic 

record keeping, document 

management, and automated 

receipt processes. 

Improving 

service or 

performance 

Planned Yes Yes Yes 

Corporate Decision 

Support System/

Commissary 

Operations 

Management System 

Mines data to produce analytical 

data on commissary operations. 

Provides information such as what 

items stores are selling and helps 

determine whether cashiers are 

being honest. 

Improving 

service or 

performance 

Operational No No No 

Defense Information Systems Agency 

Enterprise Business 

Intelligence System 

Will replace the current 

management information 

environment, which includes 

operations, reporting, billing, 

statistics, and other management 

information activities. 

Improving 

service or 

performance 

Planned No No No 
Appendix IV

Inventories of Efforts

Page 30 GAO-04-548 Data Mining

Defense Intelligence Agency 

Insight Smart 

Discovery 

Will be a data mining knowledge 

discovery tool to work against 

unstructured text. Will categorize 

nouns (names, locations, events) 

and present information in images. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Planned Yes No Yes 

Verity K2 Enterprise Mines data from the intelligence 

community and Internet searches 

to identify foreign terrorists or U.S. 

citizens connected to foreign 

terrorism activities. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes Yes Yes 

PATHFINDER Is a data mining tool developed for 

analysts that provides the ability to 

analyze government and private 

sector databases rapidly. It can 

compare and search multiple 

large databases quickly. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes No Yes 

Autonomy Is a large search engine tool that 

is used to search hundreds of 

thousands of word documents. Is 

used for the organization and 

knowledge discovery of 

intelligence. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational No No Yes 

Department of the Air Force 

ANG Data 

Warehouse—

Guardian 

Will be used to measure military 

readiness. It incorporates 

information on all disciplines to 

provide management information 

needed to assess military 

readiness.

Measuring 

military 

readiness 

Planned Yes No No 

Integrated Space 

Warfare Center 

(SWC) Information 

System 

Will be an internal database 

containing information on all 

development/execution activities 

within the SWC. Will be used by all 

management and analyst 

personnel to track and align the 

center’s activities to warfighter 

needs, report on execution status, 

financial status, schedule status, 

and performance measurements. 

Improving 

service or 

performance 

Planned Yes No No 

Safety Automated 

System (SAS) 

Will query databases to find 

automation mishaps. Governed by 

Directive 920124 and will allow for 

the investigation and reporting of 

identified automation mishaps. 

Improving 

safety 

Planned Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 31 GAO-04-548 Data Mining

Enterprise Business 

System 

Will support strategic planning, 

assist in building scientific and 

technical budgets for the Air 

Force, and serve as a launch point 

for all new programs. Research 

and development case files will be 

maintained for 75 years; the 

activity indexes, catalogs, and 

tracks these files. 

Improving 

service or 

performance 

Planned No No Yes 

Genomic and 

Proteomic Results 

Analysis 

Analyzes National Institutes of 

Health’s genetic data.

Analyzing 

scientific and 

research 

information 

Operational No No Yes 

IG Corporate 

Information System

Enhances combat readiness and 

mission capabilities for Air Combat 

Command units and commanders. 

It assists in preparing for and 

conducting inspections.

Improving 

service or 

performance

Operational Yes No No

Computer Network 

Defense System 

Evaluates network activities to 

create rules for intrusion detection 

system signature sets. 

Improving 

information 

security 

Operational No No No 

FAME Will serve as a central repository 

for Air Force manpower 

information. Will track manpower 

and unit authorization funding. 

Managing 

human 

resources 

Planned No No Yes 

Resource Wizard Serves as a manpower tracking 

system. Tracks positions and 

captures data for specific funding 

purposes. 

Improving 

service or 

performance 

Operational No No No 

Government 

Purchase Card 

Is used in overseeing purchases 

made by Air Force personnel with 

government-provided credit cards. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes No 

Ambulatory Data 

System Queries 

Tracks the initial diagnosis of 

patients with the results of further 

testing and diagnosis. Allows for 

early notification of diseases and 

injuries. 

Monitoring 

public health 

Operational Yes No No 

Modus Operandi 

Database 

Is an investigative tool used to 

identify and track trends in 

criminal behavior. It links 

characteristics of crimes and 

provides details on crime scenes 

and other crime factors. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 32 GAO-04-548 Data Mining

Executive Decision 

Support System 

Takes data from all functional 

metric balances. Processes charts 

and graphs to identify trends and 

to make sure goals are 

accomplished. 

Improving 

service or 

performance 

Operational No No No 

Inspire Is a tool that assists in providing a 

narrative description of all 

research and development that is 

being conducted within the Air 

Force. Provides cost and 

milestone information on research 

and development projects. 

Performing 

strategic 

planning 

Operational Yes No Yes 

Discoverer Is used to manage personnel 

records, including individual 

aliases and histories. 

Managing 

human 

resources 

Operational Yes No No 

Requirements and 

Concepts System 

Will serve as a repository for new 

system projects and system 

requirements. It will be available 

for consultation for information on 

all project requests and identified 

requirements. 

Improving 

service or 

performance 

Planned No No No 

Business Objects Is a commercial off-the-shelf tool 

that is used to analyze and report 

on human resources activities. 

Managing 

human 

resources 

Operational Yes No Yes 

THRMIS Uses commercial off-the-shelf 

software to maintain a data 

warehouse of integrated inventory 

and manpower data for the Total 

Force: active duty (officer and 

enlisted), Air Force Reserve, Air 

National Guard, and civilians. Is 

used to assess and analyze the 

health of the Air Force. 

Managing 

human 

resources 

Operational Yes No No 

SAS Is a Web-enabled personnel data 

system that gives authorized 

users worldwide the ability to 

tabulate demographic data on 

recruitment, promotion, and 

retention. 

Managing 

human 

resources 

Operational Yes No No 

Oracle HR Is a personnel management 

system that manages information 

for promotions, pay grades, 

clearances, and other information 

relevant to human resources. 

Managing 

human 

resources 

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 33 GAO-04-548 Data Mining

Health Modeling and 

Informatics Division 

Data Mart 

Provides information and decision 

support to the Air Force 

headquarters’ surgeon general for 

decision making, policy 

development, and resource 

allocation. It also provides 

performance information and 

analysis to medical field units in 

support of performance 

measurement objectives. 

Improving 

service or 

performance 

Operational Yes No No 

FIRST EDV (BRIO) Will deal with Air Force budgets 

and other components of its 

financial environment. Historical 

analyses and trend analyses will 

be performed on the budget 

process. 

Improving 

service or 

performance 

Planned No Yes No 

IG World Is used to store and track data and 

requirements, such as lodging and 

augmentee requirements, for the 

PAC inspector general. 

Improving 

service or 

performance 

Operational Yes No No 

Department of Defense Headquarters 

Automated 

Continuing 

Evaluation System 

Will be used to improve personnel 

security continuing evaluation 

efforts within Department of 

Defense (DOD) by identifying 

issues of security concern 

between the normal 

reinvestigation cycle for those who 

hold DOD security clearances and 

have signed a consent form that is 

still in effect. 

Managing 

human 

resources 

Operational Yes Yes Yes 

Department of the Navy 

Human Resource 

Trend Analysis 

Is used to improve Navy 

readiness. Data on personnel 

manning levels are mined to 

ensure that each Navy unit has 

the correct number of training 

personnel aboard. 

Managing 

human 

resources 

Operational No No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 34 GAO-04-548 Data Mining

U.S. Naval Academy Allows for the assessment of 

academic performance of 

midshipmen. It includes 

demographic information, 

information on grades, 

participation in sports, leadership 

positions, etc. It is an extension of 

the registrar’s system and is 

mined for comparisons and 

trends. 

Managing 

human 

resources 

Operational Yes No No 

Navy Training Master 

Planning System 

Provides overall Navy training 

information to assist in delivering 

Navy training in the most efficient 

manner. Pertinent data from 

multiple databases are 

consolidated into a single 

database that is mined. 

Managing 

human 

resources 

Operational Yes Yes No 

DHAMS 

Multidimensional 

Cubes 

Is a database that contains 

information on the time and 

attendance of 3,000 mariners 

across 120 ships. Allows 

managers to look at what people 

were doing at a particular time and 

to look across the fleet as a whole 

and compare ship activities. 

Improving 

service or 

performance 

Operational No No No 

National Cargo 

Tracking Plan Cargo 

Tracking Division 

Is used to conduct predictive 

analysis for counterterrorism, 

small weapons of mass 

destruction proliferation, narcotics, 

alien smuggling, and other highinterest activities involving 

container shipping activity. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational No Yes No 

Supply Management 

System 

Multidimensional 

Cubes 

Reduces downtime on ships by 

allowing for the analysis of ship 

parts information. The data 

warehouse contains data on every 

part that has been ordered since 

the 1980s, and has 

multidimensional information on 

each part. Failure rates can be 

calculated and improvements can 

be identified. 

Improving 

service or 

performance 

Operational No No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 35 GAO-04-548 Data Mining

Type Commanders 

Readiness 

Management System 

Is designed to provide a fully 

integrated environment for online 

analytical processing of readiness 

indicators. Examples of readiness 

indicators include status of 

supplies available, equipment in 

operation, health status, and 

capabilities of the crew. 

Measuring 

military 

readiness 

Operational No No Yes 

FATHOM (APMC—

Human Resources) 

Will be an internal program and 

project tool used to improve 

staffing, recruiting, and managing 

day-to-day operations. 

Managing 

human 

resources 

Planned Yes No No 

Navy Training Quota 

Management System 

Is used for planning and 

forecasting training needs based 

on skill requirements. 

Improving 

service or 

performance 

Operational No No Yes 

National Geospatial-Intelligence Agency 

OLAP (On-Line 

Analytical 

Processing) 

Will provide aggregations of 

imagery system performance data 

for management officers and 

senior source decision makers to 

characterize system performance 

and contribution to intelligence 

issues of national priority. 

Improving 

service or 

performance 

Planned No No No 

CITO Data Mining Will evaluate and identify imagery 

system performance trends for 

optimization, monitoring, or 

reengineering. 

Improving 

service or 

performance 

Planned No No No 

Information 

Relevance Prototype 

Will establish an information 

relevancy prototype to serve as a 

framework for community 

evaluation of commercial 

information relevance 

approaches, methods, and 

technology. The term information 

relevance refers to the ability of 

users to receive or extract, then 

display and describe, information 

with measurable satisfaction 

according to their need. 

Improving 

service or 

performance 

Planned No No No 

U.S. Marine Corps 

Operational Data 

Store Enterprise 

Is used for workforce planning. Managing 

human 

resources 

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 36 GAO-04-548 Data Mining

Source: Department of Defense.

Global Combat 

Support Systems—

Marine Corps 

Will be a physical implementation 

of the IT enterprise architecture 

designed to support both 

improved and enhanced marine 

air/ground task force combat 

service support functions and 

commander and combatant 

commander joint task force 

combatant support information 

requirements. Data mining will 

allow for interoperability with 

legacy Marine Corps systems and 

allow for a shared data 

environment. 

Improving 

service or 

performance 

Planned No Yes No 

Total Force Data 

Warehouse 

Is a system whose primary 

purpose is workforce planning and 

workforce policy decision making. 

It contains current (after 30 days) 

and historical workforce data. 

Managing 

human 

resources 

Operational Yes No No 

Marine Corps 

Recruiting 

Information Support 

System

Is a Web-based information 

system used for managing assets 

and tracking enlisted and officer 

accessions into the Marine Corps.

Managing 

human 

resources

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 37 GAO-04-548 Data Mining

Table 5: Department of Education’s Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Citizenship of PLUS 

Loan Borrowers—

National Student 

Loan Data Systems 

Looks for issues regarding 

citizenship among its PLUS loan 

borrowers. Flags records based 

on selected criteria and requests 

additional information from 

schools. 

Improving 

service or 

performance 

Operational Yes Yes Yes

Foreign Schools 

Initiatives National 

Student Loan Data 

System/Central 

Processing 

Is a proactive investigation effort 

that looks at whether financial aid 

was granted individuals attending 

foreign institutions during periods 

of nonenrollment. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes 

Professional 

Judgment Practices: 

Title IV Pell Grants, 

National Student 

Loan Data 

Used to determine when 

professional judgment has been 

exercised for “special” situations 

where families cannot afford 

college expenses. 

Improving 

service or 

performance

Operational Yes Yes Yes

Title IV Applicant— 

Death Database 

Match 

Compares Department of 

Education data with the Social 

Security Administration’s death 

database to detect fraud or 

criminal activity. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No Yes 

Title IV Loans with 

No Applications 

Will compare information from the 

Free Application for Federal 

Student Aid Program with the 

Federal Family Education Loan 

Program to identify fraud. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 

OIG—Project 

Strikeback 

Compares Department of 

Education and Federal Bureau of 

Investigation data for anomalies. 

Also verifies personal identifiers. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes No Yes 

Accuracy of U.S. 

Department of 

Education Personal 

Data 

Audits and verifies personal 

information that is contained in the 

Department of Education’s 

personal data system. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No Yes 

Impact of Cohort 

Default Rate 

Redefinition—

National Student 

Loan Data System 

Audits data to determine the 

impact of legislation that extended 

the college loan repayment default 

period from 180 to 270 days. 

Legislative 

impact 

Operational Yes No No 
Appendix IV

Inventories of Efforts

Page 38 GAO-04-548 Data Mining

CheckFree 

Software/Purchase 

Card Program 

Takes monthly billing information 

from the Bank of America to 

create reports on purchases, 

purchase quantity, and frequency 

of purchases. Data are mined for 

instances of fraud or abuse. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes No 

Improper Pell Grant 

Payment Activity 

Will compare Pell Grants issued 

with the amounts received and 

look at the eligibility of grant 

recipients. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 

Title IV Identity Theft 

Initiative 

Helps identify patterns and trends 

in identity theft cases involving 

loans for education. Provides an 

investigative resource for victims 

of identity theft. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No No 

Title IV Applicant—

Use of Multiple 

Addresses/Central 

Processing System 

Reviews addresses listed on Title 

IV applications to see if they are 

valid. For example, jails or 

employment addresses are not 

considered valid addresses. 

Improving 

service or 

performance 

Operational Yes No Yes 

Lapsed 

Funds/Improper 

Draw of Federal 

Grant Proceeds 

Identifies funds that remain in the 

grants and payment processing 

system beyond the time period for 

allocating the funds. 

Improving 

service or 

performance 

Operational No No No 

Decision Support 

System with Online 

Analytical Processing 

Query 

Will support the department’s 

performance-based initiative. Will 

allow custom queries of schools 

from state and local databases for 

demographics and test scores. 

Improving 

service or 

performance 

Planned No No No 

Grant Administration 

and Payment System 

Assists in managing grant 

activities and aids in detecting 

instances of fraud or abuse in 

grant activities. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes Yes 

Budget Execution 

Support 

Uses information in the National 

Student Loan Data System and a 

sample drawn from it to estimate 

cohort distributions for financial 

activities related to the Federal 

Family Education Loan Program 

pursuant to the Credit Reform Act. 

Financial 

management 

Operational Yes No No 

Pell Grant Model 

Assumptions 

Provides estimates on the total 

cost of the Pell Grant program. It 

uses data from previous years and 

makes assumptions for future 

years. 

Financial 

management 

Operational No No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 39 GAO-04-548 Data Mining

Source: Department of Education.

National Student 

Loan Data System 

Compiles student loan information 

from the guaranteeing agencies. 

Is used for eligibility tracking and 

to calculate default rates. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No Yes 

Loan Model 

Assumptions 

Estimates the cost of loan 

programs. Also analyzes loan 

default behavior. 

Financial 

management 

Operational Yes No Yes 

Office of the 

Inspector General 

(OIG) Projects: 

Tumbleweed/ 

Snowball 

Is part of an OIG investigation to 

determine potential fraud of 

financial aid grants primarily in 

New Hampshire. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes 

Central Processing 

System 

Processes applications for student 

aid. Contains data on more than 

13 million applications. Data are 

mined for demographic trends. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No No 

Direct Loan Services 

System 

Is used to track the life of student 

direct loans and to monitor loan 

repayments. 

Improving 

service or 

performance 

Operational Yes Yes Yes 

CheckFree 

Software/Travel Card 

Program 

Uses monthly billing information 

from Bank of America to create 

reports on travel expenditures to 

look for improper use of travel 

cards. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 40 GAO-04-548 Data Mining

Table 6: Department of Energy’s Inventory of Data Mining Efforts

Source: Department of Energy.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Counterintelligence 

Automated 

Investigative 

Management System 

(CI-AIMS) 

Is an investigative management 

system used by Department of 

Energy (DOE) field sites to track 

investigative cases on individuals 

or countries that threaten DOE 

assets. Information stored in this 

database is also used to support 

federal and state law enforcement 

agencies in support of national 

security. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No No 

Autonomy Will be used to mine a myriad 

intelligence-related databases 

within the intelligence community 

to uncover criminal or terrorist 

activities relating to DOE assets. 

Detecting 

criminal 

activities or 

patterns 

Planned Yes No No 

Counterintelligence 

Analytical Research 

Data System 

(CARDS) 

Is used to log briefings and 

debriefings given to DOE 

employees who travel to foreign 

countries or interact with foreign 

visitors to DOE facilities. Data are 

mined to identify potential threats 

to DOE assets. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes 
Appendix IV

Inventories of Efforts

Page 41 GAO-04-548 Data Mining

Table 7: Department of Health and Human Services’ Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data

Agency for Healthcare Research and Quality

National Patient 

Safety Network 

Will contain reports on adverse 

medical events that are filed by 

hospitals. The planned network’s 

purpose is to take out patient 

personal identifiers and other 

items that may violate certain 

rules and create a warehouse that 

can be used by registered and 

unregistered users to evaluate and 

implement patient safety and 

quality measures. The network will 

be used to create tools that 

hospitals can use for making 

quality improvements.

Improving 

service or 

performance 

Planned No No No 

Centers for Disease Control and Prevention

BioSense Enhances the nation’s capability to 

rapidly detect bioterrorism events. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational No Yes Yes 

Department of Health and Human Services Headquarters 

DHHS Blood 

Monitoring Program 

Monitors the country’s blood 

supply by keeping an inventory on 

red blood cells and platelets and 

monitors blood supply shortages, 

the nature of the shortage, and 

size of the shortages. 

Monitoring 

public health 

Operational No Yes No 

Food and Drug Administration

Mission 

Accomplishment and 

Regulatory 

Compliance Services 

System

Is a comprehensive redesign and 

reengineering of two core missioncritical legacy systems at Food 

and Drug Administration (FDA) 

that support the regulatory 

functions that primarily take place 

in FDA's field offices.

Monitoring 

food or drug 

safety

Operational No Yes Yes
Appendix IV

Inventories of Efforts

Page 42 GAO-04-548 Data Mining

Turbo Establishment 

Inspection Report

Provides a standardized database 

of citations of regulations and 

statutes, and help investigators in 

preparing reports. It will collect 

data on specific observations 

uncovered during inspections and 

provide a more uniform format 

nationwide that will allow for 

electronic searches and statistical 

analysis to be performed by 

citation.

Improving 

safety

Operational No Yes No

Phonetic 

Orthographic 

Computer Analysis

Is a search engine that provides 

results indicating how similar two 

drug names are on a phonetic and 

orthographic basis. Its purpose is 

to help in the safety evaluation of 

proposed proprietary names to 

reduce drug name confusion after 

an application is approved by the 

FDA.

Improving 

safety

Operational No Yes No

MPRIS Data 

Warehouse

Will provide data to support end 

user ad-hoc query analysis and 

standard reporting needs. It will 

provide the foundation for a central 

reporting repository that can be 

used to populate business-specific 

data marts.

Improving 

service or 

performance

Planned No No No

Development and 

Deployment of 

Advanced Analytical 

Tools for Drug Safety 

Risk Assessment

Will develop advanced software 

tools for quantitative analysis of 

drug safety data. Medical officers 

and safety evaluators will use 

these advances in software tools.

Analyzing 

scientific and 

research 

information

Planned Yes Yes Yes

Add data mining 

capability to CFSAN 

Adverse Event 

Reporting System

Is a comprehensive system for 

tracking, reviewing, and reporting 

adverse event incidences involving 

foods, cosmetics, and dietary 

supplements. Integrating and 

centralizing the system and 

eliminating patchwork systems 

make information on these 

adverse events available to 

federal, state, and local 

governments as well as to industry 

and the public in a more timely 

and efficient manner.

Monitoring 

food or drug 

safety

Planned Yes Yes Yes

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 43 GAO-04-548 Data Mining

Source: Department of Health and Human Services.

Table 8: Department of Homeland Security’s Inventory of Data Mining Efforts

Health Resources and Services Administration 

HRSA Geospatial 

Data Warehouse 

Data warehouse that primarily 

collects programmatic, 

demographic, and statistical data. 

Improving 

service or 

performance 

Operational No Yes Yes 

Program Support Center

Employee Assistance 

Program Analysis 

Uses information from a database 

of employee assistance program 

case information that does not 

contain client personal identifiers. 

Data are mined for quality 

assurance and program 

management information that is 

used to enhance the quality and 

cost effectiveness of services. 

Improving 

service or 

performance 

Operational No No No 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data

Border and Transportation Security Directorate

Workforce Profile 

Data Mart 

Contains payroll and personnel 

data and is mined for workforce 

trends. 

Managing 

human 

resources 

Operational Yes No Yes 

Customs Integrated 

Personnel Payroll 

System Data Mart 

Is a Customs data mart contained 

within Department of Homeland 

Security’s workforce profile data 

mart. Personnel and payroll data 

are mined for workforce trends. 

Managing 

human 

resources 

Operational Yes No Yes 

Internal Affairs 

Treasury 

Enforcement 

Communications 

System Audit Data 

Mart 

Assists the Internal Affairs group by 

mining criminal activity data to 

ascertain how Customs’ employees 

are using the Treasury Enforcement 

System. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes 

Operations 

Management 

Reports Data Mart 

Assists in managing the operation 

of all ports of entry for incoming 

carriers, people, and cargo. Helps 

in making resource (people and 

equipment) allocation and 

operational improvement decisions. 

Improving 

service or 

performance 

Operational No No Yes 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 44 GAO-04-548 Data Mining

Automated Export 

System Data Mart 

Mines data on export trade in the 

U.S. and produces reports on 

historical shipping and receiving 

trends. 

Improving 

service or 

performance 

Operational No Yes Yes 

Seized Property/

Forfeitures, 

Penalties, and Fines 

Case Management 

Data Mart 

Mines data to ensure data quality 

and review work assignments. 

System has two components: one 

that processes legal cases like a 

law firm, and a second that serves 

as property and inventory control by 

tracking property seized. 

Improving 

service or 

performance 

Operational Yes No No 

Incident Data Mart Will look through incident logs for 

patterns of events. An incident is an 

event involving a law enforcement 

or government agency for which a 

log was created (e.g., traffic ticket, 

drug arrest, or firearm possession). 

The system may look at crimes in a 

particular geographic location, 

particular types of arrests, or any 

type of unusual activity. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Planned Yes Yes Yes 

Case Management 

Data Mart 

Assists in managing law 

enforcement cases, including 

Customs cases. Reviews case 

loads, status, and relationships 

among cases. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes Yes Yes 

Emergency Preparedness and Response Directorate

Enterprise Data 

Warehouse 

Will take data from multiple, 

disparate systems and integrate the 

data into one reporting 

environment. The objective of the 

effort is to allow for the reduction of 

data within the agency and to 

provide an enterprise view of 

information necessary to drive 

critical business processes and 

decisions. Data on internal human 

resources, all aspects of disaster 

management, infrastructure, 

equipment location, etc., will be 

used. 

Disaster 

response and 

recovery 

Planned Yes Yes Yes 

Information Analysis and Infrastructure Protection Directorate

Analyst Notebook I2 Correlates events and people to 

specific information

Analyzing 

intelligence 

and detecting 

terrorist 

activities

Operational Yes Yes No

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 45 GAO-04-548 Data Mining

Source: Department of Homeland Security.

Automatic Message 

Handling System 

(Verity)

Automatically takes messages from 

external agencies and routes them 

to appropriate recipients

Analyzing 

intelligence 

and detecting 

terrorist 

activities

Planned No No Yes

U.S. Coast Guard

Readiness 

Management 

System 

Assists in ensuring readiness for all 

Coast Guard missions. 

Improving 

service or 

performance

Operational Yes No No 

CG Info Provides one-stop shopping for 

Coast Guard information. It is the 

central location and common 

interface for the entire Coast Guard 

to gain near real-time access to 

data from multiple, disparate Coast 

Guard information systems. It 

provides a single interface for users 

to view mission-critical support 

data. 

Improving 

service or 

performance 

Operational Yes No Yes

U.S. Secret Service 

Criminal 

Investigation 

Division Data Mining 

Mines data in suspicious activity 

reports received from banks to find 

commonalities in data to assist in 

strategically allocating resources. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 46 GAO-04-548 Data Mining

Table 9: Department of the Interior’s Inventory of Data Mining Efforts

Source: Department of the Interior.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Minerals Management Service 

Data Mining of the 

Technical Information 

Management System 

(TIMS) Database 

Is a corporate database for oil and 

gas leases. The database is 

mined in support of policy 

development. One area of data 

mining is identification of leases 

that will be abandoned in the near 

future. Data mining has shown 

that leases with six or more 

producing wells in 1 year are 

almost never abandoned in the 

next year. Another application of 

data mining is the safety of oil and 

gas operations. For example, data 

mining has shown that accidents 

have a peak rate on Thursday 

mornings. 

Improving 

service or 

performance 

Operational Yes Yes No 
Appendix IV

Inventories of Efforts

Page 47 GAO-04-548 Data Mining

Table 10: Department of Justice’s Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Department of Justice Headquarters

Drug/Financial 

Fusion Center 

Will contain data from, and be 

used by, Organized Crime and 

Drug Enforcement Task Force 

agencies. The system will permit 

the collection and cross case 

analysis of all drug and related 

financial investigative data. 

Detecting 

criminal 

activities or 

patterns 

Planned Yes Yes Yes 

Drug Enforcement Administration

Statistical 

Management 

Analysis and 

Reporting Tool 

System (SMARTS) 

/SPSS

Is a query analysis and reporting 

tool that pulls data from many 

systems. It allows for statistical 

analyses of drug cases Drug 

Enforcement Administration’s 

statistical reporting. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No Yes

TOLLS Is a database of telephone calls 

from court ordered and approved 

wiretaps and Title III 

investigations. Information such 

as telephone numbers, time and 

date of calls, and call duration is 

captured. Data are mined for 

patterns to give leads in 

investigations of drug trafficking. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes No No 

Federal Bureau of Investigation

Secure Collaborative 

Operational 

Prototype 

Environment/

Investigative Data 

Warehouse 

Allows the FBI to search multiple 

data sources through one 

interface to uncover terrorist and 

criminal activities and 

relationships. Data sources are a 

combination of structured and 

unstructured text. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes No Yes 

Foreign Terrorist 

Tracking Task Force 

Activity

Supports the Foreign Terrorist 

Tracking Task Force that seeks to 

prevent foreign terrorists from 

gaining access to the United 

States. Data from the Department 

of Homeland Security, Federal 

Bureau of Investigation, and public 

data sources are put into a data 

mart and mined to determine 

unlawful entry and to support 

deportations and prosecutions. 

Analyzing 

intelligence 

and detecting 

terrorist 

activities 

Operational Yes Yes Yes 
Appendix IV

Inventories of Efforts

Page 48 GAO-04-548 Data Mining

Source: Department of Justice.

FBI Intelligence 

Community Data 

Marts

Is intended to take a subset of 

approved data from a data 

warehouse and make it available 

to the intelligence community.

Analyzing 

intelligence 

and detecting 

terrorist 

activities

Planned Yes No Yes

Federal Bureau of Prisons

Business Information 

Warehouse

Will be a warehouse designed to 

provide information on 

manufacturing by Federal Prison 

Industries, which runs 100 

factories in various prisons. Data 

will be mined for information on 

the manufacturing environment 

(such as information on material 

on hand, scheduling, and the 

production process) and financial 

activities.

Improving 

service or 

performance

Planned No No Yes

U.S. Marshals Service 

USMS Workload 

Modeling 

Will seek to develop a workforce 

model that will support budget 

formulation, execution, and 

resource analysis. Will be a 

planning and execution activity 

that will be used to help determine 

the quantity and location of 

required resources. 

Managing 

human 

resources 

Planned Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 49 GAO-04-548 Data Mining

Table 11: Department of Labor’s Inventory of Data Mining Efforts

Source: Department of Labor.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Dashboard Display Provides links to programs 

throughout the Department of 

Labor’s Employment Training 

Administration to provide reports 

or information on financial 

activities. 

Improving 

service or 

performance 

Operational Yes No No 

Enforcement 

Management 

System, Case 

Opening, and 

Results Analysis 

Is used to track investigations of 

violations of Title I and other 

criminal laws pertaining to 

pension and welfare rights. 

Improving 

service or 

performance

Operational Yes Yes No 

Employee 

Retirement Income 

Security Act Data 

System 

Is used to monitor compliance 

with Title I of the Employee 

Retirement Income Security Act. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No No 

Mine Safety and 

Health Administration 

Teradata Data Store 

Mines data from a data store of 

information on safety and health 

enforcement and demographic 

data for mine operations, along 

with miner accidents, injury, and 

illness data. 

Improving 

safety 

Operational Yes No Yes 

Mathematical 

Statistics Research 

Center

Will look at data from economic 

surveys to compare rates of 

nonresponse for Bureau of Labor 

Statistics.

Improving 

service or 

performance 

Planned No No No 
Appendix IV

Inventories of Efforts

Page 50 GAO-04-548 Data Mining

Table 12: Department of State’s Inventory of Data Mining Efforts

Source: Department of State.

Table 13: Department of Transportation’s Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Citibank’s Ad Hoc 

Reporting System 

Enables purchase card managers 

to track trends related to the 

usage of credit cards by 

employees in purchasing supplies 

and services for official use. 

Purchase card program is 

worldwide, and spending patterns 

and purchases are monitored for 

potential misuse or fraud. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes Yes No 

Purchase Card 

Management System 

Will involve the automation of 

internal workflow processes 

(system is in the early phases of 

development). Will use internal 

data and bank data to track trends 

and anomalies in the Department 

of State’s worldwide purchase 

card program. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes Yes No 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Department of Transportation Headquarters 

DOT IT Security 

Management System 

Will collect information to allow 

management to assess its IT 

security infrastructure. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 

National Highway Traffic Safety Administration

State Data System Analyzes, mines, and researches 

automotive crash data, such as 

statistics from rollovers of SUVs, 

from 22 states to improve highway 

safety and lessen fatalities. 

Policies can be set based on the 

data. 

Improving 

safety 

Operational No No No 
Appendix IV

Inventories of Efforts

Page 51 GAO-04-548 Data Mining

Source: Department of Transportation.

Table 14: Department of the Treasury’s Inventory of Data Mining Efforts

Fatality Analysis 

Reporting System 

(FARS) 

Helps to evaluate the 

effectiveness of motor vehicle 

safety standards and highway 

safety programs. Data are 

collected from all 50 states, the 

District of Columbia, and Puerto 

Rico and are used to evaluate and 

support highway safety. 

Improving 

safety 

Operational Yes Yes Yes 

National Automotive 

Sampling System 

Collects and mines information on 

automotive crashes. System is 

related to the Federal Motor 

Vehicle Safety Standards that 

regulate vehicle compliance items 

such as seat belts, air bags, and 

the stopping distance of brakes. 

Improving 

safety 

Operational Yes Yes No 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Financial Management Service 

Treasury Offset 

Program (TOP) 

Cleanup 

Mines data to reduce the number 

of debts listed in TOP. 

Improving 

service or 

performance 

Operational Yes No Yes 

Electronic Federal 

Tax Payment System 

(EFTPS) Marketing 

Is a free service offered by the 

Department of the Treasury for 

individuals and business 

taxpayers who pay their federal 

taxes electronically. Mining activity 

tracks enrollment, tax payment 

history, and usage trends. 

Increasing tax 

compliance 

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 52 GAO-04-548 Data Mining

Internal Revenue Service 

Planning, Analysis, 

and Decision 

Support System 

Will be a component of the 

Custodial Accounting Program, 

which is the warehouse that is 

used to query transactional data 

and produce reports. This activity 

is meant to improve reporting and 

use decision support tools. 

Improving 

service or 

performance 

Planned Yes No Yes 

Abusive Corporate 

Tax Shelter Detection 

Model 

Will model characteristics of 

corporate tax shelters and use 

models to predict corporate tax 

shelter abuse and to assess 

compliance risk in the corporate 

taxpayer population. 

Increasing tax 

compliance 

Planned Yes Yes No 

K-1 Link Analysis Will be used to detect potential tax 

evasion. 

Increasing tax 

compliance 

Planned Yes No No 

Research on the 

Population of 

Taxpayers Who 

Receive Earned 

Income Tax Credit 

Will be used to research data on 

taxpayers who receive the EITC. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 

Issue Based 

Management 

Information System 

Will provide access to a variety of 

data sources within IRS. Will 

assist in research and case work. 

Increasing tax 

compliance 

Planned No Yes No 

Electronic Fraud 

Detection System 

Mines data to evaluate and rate 

potentially fraudulent individual tax 

returns. 

Improving 

service or 

performance 

Operational Yes No No 

Reveal Will be used to detect financial 

criminal activity such as tax 

evasion. 

Detecting 

criminal 

activities or 

patterns 

Planned Yes Yes No 

Oracle Model 22 

Partnership Return 

Scoring System 

Takes information from individual 

tax returns and attempts to 

replicate judgments made by 

taxpayers to detect the likelihood 

of material errors. 

Increasing tax 

compliance 

Operational Yes No No 

SPSS Form 1120-S 

Return Scoring 

System 

Will automate the classification of 

certain corporate tax returns. 

Increasing tax 

compliance 

Planned Yes No No 

Oracle Model 33 

Partnership Scoring 

Model 

Will identify noncompliance in 

partnership returns. 

Increasing tax 

compliance 

Planned Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 53 GAO-04-548 Data Mining

Source: Department of the Treasury.

Compliance 

Laboratory 

Will identify taxpayer 

noncompliance by looking at 

groups of returns. 

Increasing tax 

compliance 

Planned Yes Yes Yes 

U.S. Mint 

Information 

Technology Intrusion 

Detection System 

Collects information on potential 

intrusions to U.S. Mint systems. 

Looks for trends in information 

reported by sensors to determine 

if illicit activity has occurred. 

Minimizes false positives. 

Improving 

information 

security 

Operational No No No 

E-Commerce Fraud 

Analysis Activity 

Attempts to identify and stop 

fraudulent activity involving stolen 

credit cards to order products over 

the Internet or via telephone. 

Fraud rating identifiers are used to 

identify areas where fraud has 

occurred and to determine the 

likelihood of fraud. Allows for 

orders to be stopped or for orders 

over a certain dollar limit to be 

stopped. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes Yes Yes 

Data Warehouse Will be an integrated, scalable, 

expandable data warehouse that 

will support business functions by 

grouping the data in subjectoriented data marts. Each 

warehouse data mart will be 

defined to integrate both internal 

and external data to provide the 

necessary information to perform 

both historical and predictive 

analysis and support numerous 

calculations. 

Improving 

service or 

performance 

Planned No No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 54 GAO-04-548 Data Mining

Table 15: Department of Veterans Affairs’ Inventory of Data Mining Efforts

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Department of Veterans Affairs Headquarters 

Veterans Affairs 

Central Incident 

Response Center 

Is used to monitor and manage 

intrusion detection and firewalls. 

Scripts are written for forensic 

analysis to go through data 

collected from system and 

network logs. 

Detecting 

criminal 

activities or 

patterns 

Operational Yes Yes No 

Purchase Card Data 

Mining (SAS) 

Reports 

Will identify patterns in purchase 

card use to identify fraud and 

misuse and to maintain good 

internal controls. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes Yes No 

Travel Card Data 

Mining (SAS) 

Reports 

Will be used to look for patterns in 

the use of travel credit cards that 

indicate misuse or fraud and to 

maintain good internal controls. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes Yes No 

Office of Inspector 

General (OIG)

Analyzes and matches (within the 

guidelines of the law) Veterans 

Affairs (VA) files, pertaining to both 

VA-provided benefits and health 

care services to detect patterns of 

waste, fraud, or abuse. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No No 

Veterans Benefits Administration 

C & P Payment Data 

Analysis 

Analyzes compensation and 

pension data to detect fraud, 

waste, and abuse. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No Yes 

C & P Large Payment 

Verification Process 

Serves as an internal control 

intended to make sure that 

payments over a certain dollar 

threshold are reviewed to detect 

potential fraud or abuse. 

Detecting 

fraud, waste, 

and abuse 

Operational Yes No No 

Veterans Health Administration 

Primary Analysis and 

Classification 

Is used mainly to discover trends, 

incidents/events, and 

vulnerabilities that may exist in VA 

hospitals. 

Improving 

safety 

Operational No No No 

Allocation Resource 

Center Database 

Is used in making resource 

allocation decisions based on the 

analysis of patient workload and 

cost data. 

Improving 

service or 

performance 

Operational Yes No No 
Appendix IV

Inventories of Efforts

Page 55 GAO-04-548 Data Mining

Source: Department of Veterans Affairs.

Veteran’s Health 

Administration (VHA) 

Financial and Clinical 

Data Mart 

Integrates patient, clinical, and 

financial data to present a unified 

management perspective and 

enable consistent reporting. 

Improving 

service or 

performance 

Operational Yes No No 

Decision Support 

System 

Is used to identify patterns of care 

and patient outcomes linked to 

resource consumption and costs 

associated with each patient 

encounter. 

Improving 

service or 

performance 

Operational Yes No No 

Top 50 

Standardization 

Listing/Managed 

Inventory System 

Is used to standardize medical 

and hospital supplies and 

equipment to (1) improve VHA’s 

bargaining position when soliciting 

bids and (2) facilitate the ability to 

move doctors among hospitals. 

Improving 

service or 

performance 

Operational No Yes No 

VISN 16 Data 

Warehouse 

Provides unified view of the VISN 

16 VA region, composed of 10 

medical centers and 30 outpatient 

clinics. The system gives a view of 

the enterprise for management 

purposes. It is mined for a variety 

of types of information such as 

patient encounters, lab tests, 

pharmacy records, etc. 

Improving 

service or 

performance 

Operational Yes No No 

(Continued From Previous Page)

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 56 GAO-04-548 Data Mining

Table 16: Environmental Protection Agency’s Inventory of Data Mining Efforts

Source: Environmental Protection Agency.

Table 17: Export-Import Bank of the United States’ Inventory of Data Mining Efforts

Source: Export-Import Bank of the United States.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Conceptual Plans to 

Design an Approach 

and System to 

Review Financial 

Data 

Will regularly review financial data 

systems for contracts, bank cards, 

and small purchases and other 

financial databases for misuse or 

fraud of Environmental Protection 

Agency’s assets. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 

Drinking Water Data 

Warehouse 

Integrates and analyzes drinking 

water information from state, 

regional, and headquarters 

sources. Includes data on water 

systems, compliance, sample 

analytical results, and audit data. 

Monitoring 

public health 

Operational Yes No Yes 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Integrated 

Information System 

Data Warehouse 

Mining for Financial 

Risk Information 

Is used to generate reports that 

describe bank lending activities 

and exposure trends. 

Improving 

service or 

performance

Operational Yes No No 
Appendix IV

Inventories of Efforts

Page 57 GAO-04-548 Data Mining

Table 18: Federal Deposit Insurance Corporation’s Inventory of Data Mining Efforts

Source: Federal Deposit Insurance Corporation.

Table 19: Federal Reserve System’s Inventory of Data Mining Efforts

Source: Federal Reserve System.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Real Estate Stress 

Test (RSST)

Is used to measure real estate 

risk. Bank examiners use data 

from the system data as part of a 

pre-examination planning process 

to assist in identifying risk 

concentrations.

Detecting risk 

in financial 

systems 

Operational No No Yes 

Determination of 

Insured Deposits 

Will support the development of a 

new system for implementing the 

deposit insurance claims. 

Improving 

service or 

performance

Planned Yes No No 

Statistical CAMELS 

Offsite Review 

Is used to rate financial 

institutions’ performance and risk 

management practices. 

Detecting risk 

in financial 

systems 

Operational No No Yes 

Growth Monitoring 

System 

Is used to identify financial 

institutions that have experienced 

significant growth. Serves as an 

early warning system for detecting 

financial institutions that might 

pose financial risk to FDIC. 

Detecting risk 

in financial 

systems 

Operational No No Yes 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Office of the 

Inspector General 

(OIG), Audit Services 

Will support audits and 

evaluations. Using ACL, queries 

will be run against the board’s 

financial and personnel systems 

to detect fraud, waste, and abuse, 

or to provide information 

supporting any aspect of an OIG 

project. 

Detecting 

fraud, waste, 

and abuse 

Planned Yes No No 
Appendix IV

Inventories of Efforts

Page 58 GAO-04-548 Data Mining

Table 20: National Aeronautics and Space Administration’s Inventory of Data Mining Efforts

Features

Organization/ system 

name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Archiving of Web 

Information at National 

Aeronautics and 

Space Administration 

(NASA) and Goddard 

Space Federal Center 

(GSFC) 

Will gather metadata on the 

GSFC Web site at NASA to 

preserve NASA legacy 

information. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

My Goddard Search—

Mining of Goddard’s 

Web environment 

Will allow Web mining of scientific 

data at Goddard Space Center. It 

is referred to as “Google for 

Goddard.” 

Analyzing 

scientific and 

research 

information 

Planned No Yes No 

NetContext Will monitor network traffic for the 

purpose of identifying bandwidth 

use, fraud, abuse, and IT securityrelated activities. 

Detecting 

fraud, waste 

and abuse 

Planned Yes No No 

Geophysics Time 

Series Analysis 

Will develop a set of algorithms to 

identify patterns within temporal 

activities. The data will be 

trajectories of objects and 

movement of objects within 

images. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

“Simmarizer” 

(Simulation-Based 

Summary/

Discovery of 

Knowledge) 

Uses data mining techniques to 

extract knowledge from 

simulators to understand 

conditions and scenarios 

regarding space missions. 

Analyzing 

scientific and 

research 

information 

Operational No No No 

Global Environmental 

and Earth Science 

Information System 

(GENESIS)

Is used to obtain information 

about global climate changes. 

Analyzing 

scientific and 

research 

information 

Operational No No Yes 

Machine Learning and 

Data Mining for 

Improved Intelligent 

Data Understanding of 

High Dimensional 

Earth Sensed Data 

Will find patterns and 

relationships in large, complex 

earth science data sets, 

specifically for rare and small 

events hidden in larger data 

signals. Will build new capabilities 

to understand NASA science 

data. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 
Appendix IV

Inventories of Efforts

Page 59 GAO-04-548 Data Mining

Distributed Data 

Mining Techniques for 

Object Discovery in 

the National Virtual 

Observatory (NVO)

Involves using a data mining tool 

set for space science research. 

Incorporates a small number of 

targeted data mining techniques 

in order to address specific NASA 

space science research 

programs. In particular, the data 

mining environment will be used 

to explore NASA’s large space 

science data collections. These 

techniques are being applied to 

astronomical object discovery, 

identification, classification, and 

interpretation across large 

multiple distributed astronomy 

data collections. 

Analyzing 

scientific and 

research 

information 

Operational No No Yes 

Diamond Eye (System 

for Mining Images) 

Analyzes large sets of images 

looking for specific features. 

Analyzing 

scientific and 

research 

information 

Operational No Yes Yes 

Data Mining of 3-D 

Numerical Model 

Forecast Output and 

Its Application to 

Atmospheric Research 

Will automate the analysis of 

weather model output, 

observation, and satellite data to 

allow for a better understanding of 

the science of weather dynamics 

and to predict future weather 

events. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

Ecological Forecasting Will develop an adaptable system 

that can be used to mine large 

volumes of scientific data, identify 

novel causal relationships in the 

data about earth system 

processes, and rapidly 

incorporate discoveries with 

biospheric models to generate 

now-casts and forecasts of 

biospheric events and conditions. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

(Continued From Previous Page)

Features

Organization/ system 

name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 60 GAO-04-548 Data Mining

Distributed Data 

Mining for Large NASA 

Databases (Earth 

Science Earth 

Observing System 

Data) 

Will research changes, trends, 

and relationships in Earth 

Observing System (EOS) data. 

The major feature of this activity is 

that it will allow for different data 

to be mined in parts and then 

merged. The capability is needed 

for instances when scientific data 

are at different locations. A 

research quality software will be 

used to allow for a communication 

system and run-time environment 

for applying a collective data 

analysis approach not bound to 

any specific platform, learning 

algorithm, or representation of 

knowledge. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

Discovery of Changes 

from the Global 

Carbon Cycle and 

Climate System Using 

Data Mining Activity 

Will detect patterns in scientific 

data that are geospatial and 

dynamic and represented as 

raster data (gridded cells of 

surfaces such as the sun’s or 

earth’s surfaces). Mining 

capabilities are being developed 

for future NASA-relevant data and 

science. 

Analyzing 

scientific and 

research 

information 

Planned No No Yes 

“AutoSciProd” 

(Automatic Generation 

of Science Products 

from Large Image 

Data Sets)

Uses statistical and image data to 

determine and improve science 

products. 

Analyzing 

scientific and 

research 

information 

Operational No No No 

Near Archive Data 

Mining of Earth 

Science Data 

Pulls data from an archive of 

earth science data and applies 

scientists’ analyses and 

algorithms to the data. 

Analyzing 

scientific and 

research 

information 

Operational No No No 

Spectral Analysis 

Automation (SAA) 

System 

Will improve the collection, 

identification, and evaluation of 

spectral data to better meet 

scientists’ requirements. 

Analyzing 

scientific and 

research 

information 

Planned No No No 

Multiple Sensor Image 

Registration, Image 

Fusion and Dimension 

Reduction Using 

Wavelets

Will be used for collaborative 

preprocessing of data and 

research on wavelets. Will 

comprise research software that 

looks at different technologies 

such as image processing and 

dimensions. 

Analyzing 

scientific and 

research 

information 

Planned No No No 

(Continued From Previous Page)

Features

Organization/ system 

name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 61 GAO-04-548 Data Mining

Source: National Aeronautics and Space Administration.

GMSEC Event 

Message Data Mining 

Task 

Will be used to determine health 

of and reasons for problems with 

satellite systems. 

Analyzing 

scientific and 

research 

information 

Planned No No No 

Intrusion Detection 

System 

Looks at all traffic that traverses 

NASA’s networks’ borders. 

Improving 

information 

security 

Operational Yes No No 

AvSP/ASMM Foreign 

Object Detection 

Toolset 

Is used with simulations to identify 

foreign object damage indicators 

for commercial jet engines. 

Analyzing 

scientific and 

research 

information 

Operational No Yes No 

Mission and Science 

Measurement and 

Discovery Systems 

Will be a basic technology 

research program that will also 

support infusion of resulting 

technologies into NASA missions. 

Purpose of the program is to 

solve the research challenge in 

extracting the most scientific 

knowledge from NASA’s space 

missions and data archives. 

Analyzing 

scientific and 

research 

information 

Planned No No No 

StarTool: Solar Active 

Region Detection 

Is used for recognition of solar 

activity in sequences of multiband 

solar images. 

Analyzing 

scientific and 

research 

information 

Operational No No No 

“Toogle” (Times-Series 

Search Engine) 

Searches for time-series data. Is 

similar to a Google search 

engine. 

Improving 

safety 

Operational No No No 

Use of Data Mining, 

Remote Sensing, and 

Geographic 

Information Systems 

for Wildfire Detection 

and Prediction

Will help the National Oceanic 

and Atmospheric Administration 

automate its fire detection 

systems and improve the 

accuracy of fire detection 

systems. 

Improving 

service or 

performance 

Planned No No Yes 

Knowledge Discovery 

and Data Mining 

Based on Hierarchical 

Image Segmentation 

Will mine data using software that 

has been developed to exploit 

information from a hierarchical 

image segmentation process. 

Analyzing 

scientific and 

research 

information 

Planned No Yes Yes 

(Continued From Previous Page)

Features

Organization/ system 

name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data
Appendix IV

Inventories of Efforts

Page 62 GAO-04-548 Data Mining

Table 21: Nuclear Regulatory Commission’s Inventory of Data Mining Efforts

Source: Nuclear Regulatory Commission.

Table 22: Office of Personnel Management’s Inventory of Data Mining Efforts

Source: Office of Personnel Management.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Licensee Event 

Report Data 

Identifies nuclear safety trends 

and patterns in commercial 

nuclear power events. 

Improving 

safety 

Operational No Yes No 

Centralized 

Information Delivery 

Will consolidate and standardize 

reporting for nuclear reactor 

regulations. 

Improving 

service or 

performance 

Planned Yes No No 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

CRIS Retirement 

Data Mining Activity 

Mines federal employee benefits 

data such as information on 

retirement and life insurance to 

assist in managing federal 

employee eligibilities and 

entitlements. 

Improving 

service or 

performance 

Operational Yes No Yes 
Appendix IV

Inventories of Efforts

Page 63 GAO-04-548 Data Mining

Table 23: Pension Benefit Guaranty Corporation’s Inventory of Data Mining Efforts

Source: Pension Benefit Guaranty Corporation.

Table 24: Railroad Retirement Board’s Inventory of Data Mining Efforts

Source: Railroad Retirement Board.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Corporate 

Performance 

Indicators and 

Analytics 

Will streamline access to 

management and operational 

performance measures and 

permit the correlation of 

performance and output 

measures. 

Improving 

service or 

performance 

Planned No No Yes 

Corporate Policy and 

Research 

Department’s 

Forecasting System 

Is a stochastic simulation model 

that incorporates historic equity 

and interest rates and bankruptcy 

possibilities to forecast scenarios 

for more than 300 pension plans 

and their related corporate 

sponsors. 

Improving 

service or 

performance

Operational No Yes Yes 

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Railroad Retirement 

Board Data Stores 

Consists of two major databases 

(payment and entitlement history 

and employment data 

maintenance) that are mined by 

actuaries to produce annual 

actuarial reports and for audit 

support and quality control. 

Improving 

service or 

performance 

Operational Yes No Yes 
Appendix IV

Inventories of Efforts

Page 64 GAO-04-548 Data Mining

Table 25: Small Business Administration’s Inventory of Data Mining Efforts

Source: Small Business Administration.

Features

Organization/ 

system name Description Purpose Status

Personal 

information

Private 

sector 

data

Other 

agency data

Loan Monitoring 

System 

Helps to identify, measure, and 

manage the risk of Small 

Business Administration’s 

portfolio. Business credit scores 

are used but individual credit 

scores are not. 

Improving 

service or 

performance 

Operational Yes Yes No 

MONSTER and 

Econometric Models 

Mines data from database that 

includes all transactions for each 

loan that affects SBA subsidy 

costs, to assist in determining 

credit subsidy rates for SBA’s 

various credit programs. 

Financial 

management 

Operational Yes No No 

(310366)
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